May 11, 2017
After Henry Ford invented the moving assembly line, manufacturing was never the same. With it, his workers were able to push out a car every 2 ½ hours instead of the 12 it used to take. (Another website said it was reduced to 90 min!) The technology quickly spread to every factory.
Now of course, an assembly line is only as fast as its slowest worker. If someone is taking extra time to bolt down that part, then everyone downstream will have to go slower too, resulting in fewer cars being made.
But, you also can’t go too fast. If you do, someone can get injured, shutting down the whole line. (Or the worker has to eat all the candy to keep up, like Lucy.)
And you want to make sure things happen in the right part of the factory. You don’t want the paint sprayer out in the open, poisoning factory workers. So, that needs to happen in a special room.
This also applies to cell processes where something complicated is built, step by enzymatic step. All the enzymes need to be at the right levels and in the right place to maximize the productivity of the whole process.
This all becomes very obvious when you try to move an enzymatic process from one beast to another. What worked perfectly before, now barely works at all.
One way to fix this is through trial and error, trying to optimize one part of the process at a time. This is incredibly time consuming!
In a new study out in Nature Communications, Awan and coworkers show one way to tweak all of the enzymatic steps involved in making penicillin at the same time in the yeast Saccharomyces cerevisiae. While this isn’t that useful for making this antibiotic (there are better ways available right now), it does show how researchers can apply the same techniques to perhaps identify and produce new antibiotics. And, it can also be applied to other unrelated enzymatic processes.
Penicillin is made in a five-step process in filamentous fungi. In the first part of the process, two enzymes create a tripeptide precursor using alpha-aminoadipic acid, cysteine, and valine, called ACV. This part of the process had been previously recapitulated in yeast, so Awan and coworkers used this as a starting point for their penicillin producing strain.
The next part of the process uses the last three enzymes and takes place in peroxisomes in filamentous fungi. These authors found that they only got penicillin when these enzymes were tagged to be sent to the peroxisome in yeast. Like a special room for spray painting cars, these enzymes need to be in the right place to make penicillin.
But this was by no stretch of the imagination an efficient penicillin-making machine. The thing managed only 90 pg/ml in the media. As Ursula from Little Mermaid might say, “Pathetic.”
Still, it is a starting point. The next step is to get the yeast to crank out more penicillin. To do this, they used a combinatorial approach to optimize the process all at once. Well, not really all at once.
First, they set out to optimize how much of the precursor ACV the yeast made. Then, they optimized how much ACV was converted to penicillin.
Awan and coworkers created a library of low copy plasmids that had the genes for the first two enzymes, pcbAB and npgA, under the control of different pairs of promoters. One plasmid, with the pTDH3 promoter driving pcbAB expression, and the pPGK1 promoter driving npgA expression, outperformed all of the others. As measured by Liquid Chromatography-Mass Spectrometry (LCMS), the yield of ACV increased from 20 to ~280 ng/ml.
Next, the authors used this new strain as a starting point for optimizing the activity of the final three enzymes using a similar approach. They used a “…one-pot combinatorial DNA assembly using Golden Gate cloning…” to make a library of around 1000 high copy plasmids where each gene was under the control of one of ten different promoters of varying strength. Using LCMS they found strains that could make 3 ng/ml of penicillin, a significant improvement over the original 90 pg/ml.
The 3 ng/ml of penicillin in the media should be high enough concentration to inhibit the growth of bacteria like Streptococcus pyogenes. So, they confirmed that their penicillin was active using growth inhibition assays.
After sequencing the plasmids, the authors saw that the best strains tended to have strong constitutive promoters driving one of the genes, pclA, and medium strength promoters driving another one of the genes, pcbC. They used a minION DNA sequencer to confirm that this was not the result of a biased library.
As a final step, they set out to optimize penicillin production and to increase the throughput of their assay. They created another library that swapped six different promoters that varied in strength from medium to high for each of the last three genes in the pathway, pclA, pcbC and penDE. Instead of using LCMS to screen for penicillin production, they used a 96 well plate-based assay that looked for inhibition of Streptococcus pyogenes growth for their 120 new strains.
They selected 12 of the highest performing strains and confirmed by LCMS that they made lots of penicillin. Five of the strains made more than 5 ng/ml, a more than 50-fold increase over their original strain.
As this concentration is still three orders of magnitude below what other organisms can currently do, this new yeast strain will not go into penicillin production any time soon. But this study gives us a way to quickly optimize antibiotic production using growth inhibition assays instead of the more cumbersome LCMS.
And it isn’t restricted to just antibiotic production. Similar combinatorial approaches can be used for almost any stepwise enzymatic process. Researchers can create libraries of plasmids where levels of enzyme vary and use the long reads of minION DNA sequencing technology to confirm that their results are not skewed by a biased library.
As usual, this is only possible as a simple, easy procedure because of the awesome power of yeast genetics (#APOYG). Researchers have the tools to use yeast to find new antibiotics and to manufacture them at a high rate, like inventing the car and the assembly line at the same time.
by Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics
Categories: Research Spotlight